COVID 19 Analysis in India¶

This dataset depicts the daily confirmed and daily deaths recorded due to COVID-19 in India.

In [ ]:
import pandas as pd
import numpy as np
data = pd.read_csv("covid19_india.csv")
print(data.head())
data.describe()
              Date    Date_YMD  Daily Confirmed  Daily Deceased
0  30 January 2020  2020-01-30                1               0
1  31 January 2020  2020-01-31                0               0
2  1 February 2020  2020-02-01                0               0
3  2 February 2020  2020-02-02                1               0
4  3 February 2020  2020-02-03                1               0
Out[ ]:
Daily Confirmed Daily Deceased
count 720.000000 720.000000
mean 52637.915278 675.901389
std 78522.746943 932.895333
min 0.000000 0.000000
25% 10419.250000 159.000000
50% 27383.000000 387.000000
75% 54300.750000 734.750000
max 414280.000000 6139.000000
In [ ]:
import plotly.express as px
fig = px.bar(data, x='Date_YMD', y='Daily Confirmed')
fig.show(renderer='notebook')
In [ ]:
fig = px.bar(data, x='Date_YMD', y='Daily Deceased')
fig.show(renderer='notebook')

Overlaying the Confirmed and Death graphs over each other to see trends¶

In [ ]:
import plotly.express as px
from plotly.subplots import make_subplots
import plotly.graph_objects as go

# Create figure with subplots
fig = make_subplots(specs=[[{"secondary_y": True}]])

# Add traces for Daily Confirmed and Daily Deceased
fig.add_trace(
    go.Bar(x=data['Date_YMD'], y=data['Daily Confirmed'], name='Daily Confirmed'),
    secondary_y=False,
)

fig.add_trace(
    go.Bar(x=data['Date_YMD'], y=data['Daily Deceased'], name='Daily Deceased'),
    secondary_y=True,
)

# Update layout
fig.update_layout(
    title_text="Daily Confirmed and Daily Deceased Cases",
    xaxis_title="Date",
    yaxis_title="Daily Confirmed Cases",
    yaxis2_title="Daily Deceased Cases",
)

# Show figure
fig.show(renderer='notebook')
In [ ]: